Norio Shinkai

772 total citations · 1 hit paper
14 papers, 408 citations indexed

About

Norio Shinkai is a scholar working on Molecular Biology, Cancer Research and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Norio Shinkai has authored 14 papers receiving a total of 408 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Cancer Research and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Norio Shinkai's work include Cancer Genomics and Diagnostics (5 papers), Epigenetics and DNA Methylation (4 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Norio Shinkai is often cited by papers focused on Cancer Genomics and Diagnostics (5 papers), Epigenetics and DNA Methylation (4 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). Norio Shinkai collaborates with scholars based in Japan, United Kingdom and India. Norio Shinkai's co-authors include Ken Asada, Masaaki Komatsu, Ryuji Hamamoto, Syuzo Kaneko, Ken Takasawa, Hidenori Machino, Kazuma Kobayashi, Amina Bolatkan, Satoshi Takahashi and Ryo Shimoyama and has published in prestigious journals such as Scientific Reports, Cancers and Briefings in Bioinformatics.

In The Last Decade

Norio Shinkai

13 papers receiving 398 citations

Hit Papers

1 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review

2024 58 citations Satoshi Takahashi, Yusuke Sakaguchi et al. Journal of Medical Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Norio Shinkai Japan	10	150	124	106	76	58	14	408
Amina Bolatkan Japan	10	147 1.0×	138 1.1×	114 1.1×	78 1.0×	61 1.1×	12	413
Kevin Boehm United States	5	164 1.1×	135 1.1×	128 1.2×	89 1.2×	47 0.8×	8	433
Ken Takasawa Japan	15	188 1.3×	264 2.1×	142 1.3×	123 1.6×	78 1.3×	27	652
Luoting Zhuang United States	2	170 1.1×	55 0.4×	157 1.5×	52 0.7×	69 1.2×	5	336
Felipe Giuste United States	11	82 0.5×	225 1.8×	94 0.9×	34 0.4×	72 1.2×	36	519
Adrian Levine Canada	7	105 0.7×	53 0.4×	102 1.0×	31 0.4×	25 0.4×	25	287
Jasleen Grewal Canada	11	87 0.6×	209 1.7×	101 1.0×	98 1.3×	28 0.5×	19	490
Marko van Treeck Germany	11	178 1.2×	45 0.4×	209 2.0×	60 0.8×	50 0.9×	21	345
Ramón Viñas United Kingdom	6	187 1.2×	134 1.1×	263 2.5×	133 1.8×	41 0.7×	9	523
Randy Van Ommeren Canada	7	105 0.7×	80 0.6×	105 1.0×	27 0.4×	131 2.3×	10	364

Countries citing papers authored by Norio Shinkai

Since Specialization

Citations

This map shows the geographic impact of Norio Shinkai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Norio Shinkai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Norio Shinkai more than expected).

Fields of papers citing papers by Norio Shinkai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Norio Shinkai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Norio Shinkai. The network helps show where Norio Shinkai may publish in the future.

Co-authorship network of co-authors of Norio Shinkai

This figure shows the co-authorship network connecting the top 25 collaborators of Norio Shinkai. A scholar is included among the top collaborators of Norio Shinkai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Norio Shinkai. Norio Shinkai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

Shinkai, Norio, Ken Asada, Hidenori Machino, et al.. (2025). SEgene identifies links between super enhancers and gene expression across cell types. npj Systems Biology and Applications. 11(1). 49–49.

Takahashi, Satoshi, Yusuke Sakaguchi, Ken Takasawa, et al.. (2024). Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. Journal of Medical Systems. 48(1). 84–84. 58 indexed citations breakdown →

Hamamoto, Ryuji, Ken Takasawa, Norio Shinkai, et al.. (2023). Analysis of super-enhancer using machine learning and its application to medical biology. Briefings in Bioinformatics. 24(3). 9 indexed citations

Shozu, Kanto, Syuzo Kaneko, Norio Shinkai, et al.. (2022). Repression of the PRELP gene is relieved by histone deacetylase inhibitors through acetylation of histone H2B lysine 5 in bladder cancer. Clinical Epigenetics. 14(1). 147–147. 9 indexed citations

Hamamoto, Ryuji, Ken Takasawa, Hidenori Machino, et al.. (2022). Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Briefings in Bioinformatics. 23(4). 31 indexed citations

Dozen, Ai, Kanto Shozu, Norio Shinkai, et al.. (2022). Tumor Suppressive Role of the PRELP Gene in Ovarian Clear Cell Carcinoma. Journal of Personalized Medicine. 12(12). 1999–1999. 11 indexed citations

Asada, Ken, Syuzo Kaneko, Ken Takasawa, et al.. (2021). Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Frontiers in Oncology. 11. 666937–666937. 36 indexed citations

Kaneko, Syuzo, Ken Takasawa, Ken Asada, et al.. (2021). Epigenetic Mechanisms Underlying COVID-19 Pathogenesis. Biomedicines. 9(9). 1142–1142. 7 indexed citations

Asada, Ken, Masaaki Komatsu, Ryo Shimoyama, et al.. (2021). Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. Journal of Personalized Medicine. 11(9). 886–886. 21 indexed citations

10.

Asada, Ken, Ken Takasawa, Hidenori Machino, et al.. (2021). Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines. 9(11). 1513–1513. 14 indexed citations

11.

Takahashi, Satoshi, Ken Asada, Ken Takasawa, et al.. (2020). Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data. Biomolecules. 10(10). 1460–1460. 62 indexed citations

12.

Asakawa, Jun‐ichi, Mayumi Nishimura, Tony Kuo, et al.. (2020). Characteristics of induced mutations in offspring derived from irradiated mouse spermatogonia and mature oocytes. Scientific Reports. 10(1). 37–37. 21 indexed citations

13.

Fujikawa, Yoshihiro, Tomoko Ishikawa‐Fujiwara, Tony Kuo, et al.. (2020). Involvement of Rev1 in alkylating agent‐induced loss of heterozygosity in Oryzias latipes. Genes to Cells. 25(2). 124–138. 3 indexed citations

14.

Hamamoto, Ryuji, Kruthi Suvarna, Masayoshi Yamada, et al.. (2020). Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers. 12(12). 3532–3532. 126 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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